Background Long working hours, as a common risk factor for occupational stress, is closely related to the occurrence of depressive symptoms. Understanding how long working hours affect occupational stress and depressive symptoms will inform occupational health interventions. Objective To quantify the impact of long working hours exposure on occupational stress and depressive symptoms among Internet industry employees, translate black-box outputs into actionable insights, and demonstrate the value of interpretable machine learning for early-warning occupational-health surveillance. Methods A dataset was derived from a cross-sectional survey involving 2866 internet industry employees in China. This survey was part of the project Risk Assessment Of Long Working Hour Exposure And Its Adverse Health Effects, conducted by the National Institute for Occupational Health and Poisoning Control, Chinese Center for Disease Control and Prevention, from 2021 to 2023. Working hours, occupational stress and depressive symptoms were quantified with a set of structured questionnaires including the Core Occupational Stress Scale and the Patient Health Questionnaire. Pairwise associations were screened by Mantel tests and variance-inflation factors. Key predictors identified through feature selection were fed into six machine-learning risk-prediction models. Visual interpretation was provided by feature importance, Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME), while directed causal effects and intervention impacts of prolonged working hours exposure on occupational stress and depressive symptoms were dissected with causal explanation of features techniques. Results The positive rates of occupational stress and depressive symptoms among internet employees were 12.9% and 77.8% respectively. Twelve core features for occupational stress and nine for depressive symptoms were retained after selection. After these features were supplied to six predictive algorithms and evaluated on five metrics, the Light Gradient Boosting Machine (LGBM) achieved the highest accuracy—0.89 for occupational stress and 0.79 for depressive symptoms on the hold-out test set. The feature-importance rankings converged on fatigue accumulation and life satisfaction as dominant drivers for both outcomes, whereas weekly working hours and daily overtime emerged as the principal exposure-related predictors. The SHAP summary plots revealed that longer weekly hours and daily overtime systematically elevated the probability of occupational stress. The causal feature explanation further quantified that ascending one category in weekly working hours increased the probability of occupational stress by 7.04%. Conclusion Exposure to long working hours is associated with both occupational stress and depressive symptoms among internet industry employees. Interpretable machine-learning frameworks translate these associations into transparent, defensible drivers, enabling precise identification of the pivotal factors and their interplay. This evidence base equips occupational-health practitioners with actionable insights for designing targeted prevention and intervention strategies.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Traditional Chinese medicine (TCM) is a well-accepted therapy for atopic dermatitis (AD). However, there are currently no evidence-based guidelines integrating TCM and Western medicine for the treatment of AD, limiting the clinical application of such combined approaches. Therefore, the China Association of Chinese Medicine initiated the development of the current guideline, focusing on key issues related to the use of TCM in the treatment of AD. This guideline was developed in accordance with the principles of the guideline formulation manual published by the World Health Organization. A comprehensive review of the literature on the combined use of TCM and Western medicine to treat AD was conducted. The findings were extensively discussed by experts in dermatology and pharmacy with expertise in both TCM and Western medicine. This guideline comprises 23 recommendations across seven major areas, including TCM syndrome differentiation and classification of AD, principles and application scenarios of TCM combined with Western medicine for treating AD, outcome indicators for evaluating clinical efficacy of AD treatment, integration of TCM pattern classification and Western medicine across disease stages, daily management of AD, the use of internal TCM therapies and proprietary Chinese medicines, and TCM external treatments. Please cite this article as: Du XR, Wu MY, Tao MC, Lin Y, Gu CY, Wu MF, Cao Y, Chen DC, Li W, Wang HW, Wang Y, Wang Y, Lu HZ, Liu X, Su XF, Li FL. Clinical practice guidelines for the diagnosis and treatment of atopic dermatitis with integrative traditional Chinese and Western medicine. J Integr Med. 2025; 23(6):641-653.
Dermatitis, Atopic/drug therapy* ; Humans ; Medicine, Chinese Traditional/methods* ; Integrative Medicine ; Drugs, Chinese Herbal/therapeutic use* ; Practice Guidelines as Topic

Background Occupational stress has emerged as a critical public health concern affecting the physical and mental well-being of workers in the manufacturing sector. However, researchers typically evaluate its core driver—cumulative fatigue—using a crude binary “present/absent” variable, thereby overlooking the high-dimensional complexity and heterogeneity inherent in fatigue characteristics. This oversimplification constrains both the precision and predictive performance of occupational stress risk assessment model. Objective Leveraging a data-driven approach, to survey data on cumulative fatigue among manufacturing employees, and then use this new classification to develop and validate an occupational stress prediction model, with an ultimate aim of enhancing the accuracy and effectiveness of occupational stress assessment. Methods A set of cross-sectional survey data on 3871 manufacturing employees in 2021 were derived from the “Long working hours exposure and its adverse health effect risk assessment” program of the National Institute for Occupational Health and Poison Control, Chinese Center for Disease Control and Prevention. Occupational stress was assessed using the Core Occupational Stress Measurement Scale, while cumulative fatigue was evaluated via the Worker’s Self-Diagnostic Questionnaire for Fatigue Accumulation. Boruta method was applied to screen core variables from 20 occupational stress influencing factors. Dimensionality reduction of cumulative fatigue features was performed using a Bayesian sparse autoencoder (BASE), followed by comparison and application of clustering methods to achieve multi-class classification of the reduced features. Six machine learning classification models were then selected including logistic regression, support vector machine, decision tree, random forest, adaptive boosting, and lightweight gradient boosting machine (LightGBM) to construct and compare two occupational stress prediction models involving cumulative fatigue combined with ten other core variables: one utilizing the original binary cumulative fatigue label as a core variable, and another employing the novel fatigue classification proposed herein as a core variable. Results The positive rate of occupational stress among the manufacturing employees was 38.9%. The Boruta method identified 11 core variables of occupational stress: depressive symptoms, cumulative fatigue, age, length of service, tenure in current position, average weekly working hours, average daily overtime hours, average monthly income, low levels of exercise, life satisfaction, and sleep quality. The BSAE reduced the original cumulative fatigue factors to 12 dimensions, while the K-means clustering grouped the reduced fatigue features into three categories: no fatigue, moderate fatigue, and sever fatigue. Six occupational stress prediction models were constructed using the three-category labels of cumulative fatigue and ten additional factors. The results indicated that the LightGBM model performed best, achieving an area under the curve (AUC) of 0.78, an accuracy of 0.77, and an F1 score of 0.72. Compared with the model incorporating the traditional binary labels for cumulative fatigue, the best prediction AUC (0.72), accuracy (0.66), and F1 score (0.65) improved by 6%, 11%, and 7%, respectively. Conclusion Cumulative fatigue is a significant predictor of occupational stress among manufacturing employees. Applying data dimensionality reduction combined with three-class cluster analysis to characterize cumulative fatigue improves the performance of occupational stress prediction models. From a data-driven perspective, machine learning methods demonstrate strengths in processing complex datasets, capturing nonlinear relationships, and generating predictions based on key variables. This study therefore confirms that refined processing of core factors substantially enhances the predictive capability of machine learning models in assessing occupational stress risk in the manufacturing workforce.

The xylitol dehydrogenase(XDH)is a crucial enzyme involved in the xylose utilization in pentose-catabolizing yeasts and fungi.In addition to producing xylulose,XDH can also be employed to develop a biosensor for monitoring xylitol concentration.In this study,the gene encoding the thermophilic fungus Talaromyces emersonii XDH(TeXDH)was heterologously expressed in Escherichia coli BL21(DE3)at 16 ℃ in the soluble form.Recombinant TeXDH with high purity was purified by using a Ni-NTA affinity column.Size-exclusion chromatography and SDS-PAGE analysis demonstrated that the puri-fied recombinant TeXDH exists as a native trimer with a molecular mass of approximately 116 kD,and is composed of three identical subunits,each with a molecular weight of around 39 kD.The TeXDH strictly preferred NAD+as a coenzyme to NADP+.The optimal temperature and pH of the TeXDH were 40 ℃and 10.0,respectively.After EDTA treatment,the enzyme activity of TeXDH decreased to 43.26％of the initial enzyme activity,while the divalent metal ions Mg2+or Ca2+could recover the enzyme activity of TeXDH,reaching 103.32％and 110.69％of the initial enzyme activity,respectively,making them the optimal divalent metal ion cofactors for TeXDH enzyme.However,the divalent metal ions of Mn2+,Ni2+,Cu2+,Zn2+,Co2+,and Cd2+significantly inhibited the activity of TeXDH.ICP-MS and molecular doc-king studies revealed that 1 mol/L of TeXDH bound 2 mol/L Zn2+ions and 1 mol/L Mg2+ion.Further-more,TeXDH exhibited a high specificity for xylitol,laying the foundation for the development of future xylitol biosensors.

BACKGROUND:Neurodegenerative diseases are closely related to the imbalance of mitochondrial autophagy regulation.Previous studies by the research group have shown that lycium barbarum polysaccharide has neuroprotective effects,but whether it can improve the damage of SH-SY5Y cells induced byβ-amyloid protein 1-42 by regulating mitochondrial autophagy is still unclear.OBJECTIVE:To explore the protective effect and mechanism of Lycium barbarum polysaccharide on SH-SY5Y cells induced by β-amyloid protein 1-42.METHODS:An Alzheimer's disease cell model was established by inducing SH-SY5Y cells with β-amyloid protein 1-42,and then intervening with Lycium barbarum polysaccharide.SH-SY5Y cells were divided into three groups:control group,β-amyloid protein 1-42 group(20 μmol/L β-amyloid protein 1-42 for 24 hours),and Lycium barbarum polysaccharide group(1 g/L Lycium barbarum polysaccharide was added 1 hour in advance to form a protective effect,and then 20 μmol/L β-amyloid protein 1-42 was added to intervene with Lycium barbarum polysaccharide for 24 hours).CCK8 assay was used to detect cell viability.Mitochondrial membrane potential was detected by JC-1.TUNEL staining was used to detect cell apoptosis.Immunofluorescence and western blot assay were used to detect the expression of synaptic,apoptosis,and mitophagy-related indicators.RESULTS AND CONCLUSION:(1)Compared with the control group,the cell viability of the β-amyloid protein 1-42 group decreased(P＜0.05);cell apoptosis rate increased(P＜0.05);mitochondrial membrane potential decreased(P＜0.05);the expressions of pro-apoptotic proteins Bax and Caspase3 increased(P＜0.05);the expression of anti-apoptotic protein Bcl-2 decreased(P＜0.05);the expression levels of synaptic-related proteins Syn and PSD-95 decreased(P＜0.05);the expression levels of mitochondrial autophagy-related proteins Pink1,LC3A/B,Parkin,and Beclin-1 decreased(P＜0.05);and the expression of P62 increased(P＜0.05).(2)Compared with the β-amyloid protein 1-42 group,the cell viability in the Lycium barbarum polysaccharide group was increased(P＜0.05);the apoptosis rate was decreased(P＜0.05);the mitochondrial membrane potential was increased(P＜0.05);the expression levels of Bax and Caspase3 were decreased(P＜0.05);the expression of Bcl-2 was increased(P＜0.05);the expressions of Syn and PSD-95 were increased(P＜0.05);the expression levels of Pink1,LC3A/B,Parkin,and Beclin-1 were increased(P＜0.05),and the expression of P62 was decreased(P＜0.05).These findings indicate that Lycium barbarum polysaccharide may inhibit β-amyloid protein 1-42-induced damage to SH-SY5Y cells by regulating mitophagy,reduce cell apoptosis,and increase neuronal synaptic plasticity.

BACKGROUND:A large number of studies have shown that neurodegenerative diseases are closely related to oxidative stress injury and the imbalance of mitochondrial dynamics.Lycium barbarum polysaccharides have a neuroprotective effect.However,it is not clear whether lycium barbarum polysaccharides can ameliorate apoptosis induced by oxidative stress injury by regulating abnormal mitochondrial dynamics.OBJECTIVE:To study the effect of lycium barbarum polysaccharides on apoptosis induced by H2O2 in SH-SY5Y human neuroblastoma cells.METHODS:SH-SY5Y cells were cultured in three groups.The control group was cultured for 24 hours.The hydrogen peroxide group was treated with H2O2 for 24 hours,and the lycium barbarum polysaccharide group was treated with lycium barbarum polysaccharide for 2 hours and then treated with H2O2 for 24 hours.After treatment,the levels of malondialdehyde,glutathione,and superoxide dismutase in the precipitation of the cells were detected by kit.Mitochondrial membrane potential was detected by JC-1 kit.Cell viability was detected by MTT assay.Apoptosis was detected by TUNEL.The expression levels of mitochondrial dynamics-related proteins (phosphorylated promoter protein 1,mitochondrial fission protein 1,mitochondrial fusion protein 1,mitochondrial fusion protein 2,and optic atrophy protein 1) and apoptotic proteins (Bax,Bcl-2,and Caspase-3) were detected by immunofluorescence staining and western blot assay.RESULTS AND CONCLUSION:(1) Compared with the control group,the levels of malondialdehyde were increased (P<0.05),and the levels of superoxide dismutase and glutathione were decreased (P<0.05) in the H2O2 group.Compared with the H2O2 group,the malondialdehyde level was decreased (P<0.05),and the superoxide dismutase and glutathione levels were increased (P<0.05) in the lycium barbarum polysaccharide group.(2) The mitochondrial membrane potential in the H2O2 group was lower than that in the control group (P<0.05),and that of lycium barbarum polysaccharide group was higher than that of the H2O2 group (P<0.05).(3) Compared with the control group,the apoptosis rate and the expression of Bax and Caspase-3 protein were increased (P<0.05),while the cell viability and the expression of Bcl-2 protein were decreased (P<0.05) in the H2O2 group.Compared with the H2O2 group,the apoptosis rate and the expression of Bax and Caspase-3 protein were decreased (P<0.05),while the cell viability and the expression of Bcl-2 protein were increased (P<0.05) in the lycium barbarum polysaccharide group.(4) Compared with the control group,the protein expression levels of phosphorylated promoter protein 1 and mitochondrial fission protein 1 were increased (P<0.05),and the protein expression levels of mitochondrial fusion protein 1,mitochondrial fusion protein 2,and optic atrophy protein 1 were decreased (P<0.05) in the H2O2 group.Compared with the H2O2 group,the protein expression levels of phosphorylated promoter protein 1 and mitochondrial fission protein 1 were decreased (P<0.05),and the protein expression levels of mitochondrial fusion protein 1,mitochondrial fusion protein 2,and optic atrophy protein 1 were increased (P<0.05) in the lycium barbarum polysaccharide group.(5) These results indicate that lycium barbarum polysaccharide can improve SH-SY5Y cell apoptosis caused by oxidative stress damage by regulating mitochondrial dynamics.

Objective To establish a high-throughput non-targeted screening and prioritization method for emerging pollutants(EPs)in sewage using direct injection high-resolution mass spectrometry(HRMS).Methods The sewage samples were filtered by membrane filter and directly subjected to the liquid chromatography-time-of-flight mass spectrometer based on a method modified from our previous study.A C18 chromatographic column was applied for a gradient elution separation,and accurate mass and mass spectral fragment information were obtained through the MS full scan mode and MS/MS DIA data collection mode.After peak detection and alignment,the features from the raw data through open source software MZmine 3,and then high-throughput screening strategies such as MassBank and PubChem databases were used for compound annotation.Finally,the candidate features were confirmed with chemical standards by compared their retention time and mass spectrum fragmentation ion peaks.Results 13 EPs were identified,including 7 industrial chemicals,4 pharmaceuticals,1 pesticide and 1 metabolite.High detection rates were observed for metformin(86.2％),2-hydroxybenzothiazole(79.3％),1,2-benzisothiazole-3-one(72.4％),and 1,2-benzisothiazole-3-one(72.4％).The quantitative concentration range of EPs was 1.37～19.05 ng/mL,with the high concentrations observed for melamine(19.05 ng/mL)and furosemide(18.49 ng/mL).Ecological risk assessment identified 1,2-benzisothiazol-3-one,4-aminoacetophenone,creatinine,2-hydroxybenzothiazole,and furosemide as key pollutants.Conclusion This direct injection coupled with HRMS workflow enables efficient non-targeted screening and prioritization of emerging EPs in sewage samples,highlighting five ecotoxicologically critical EPs.The methodology enhances environmental monitoring capabilities and provide critical technical support for interdisciplinary research such as environmental forensics and health risk assessment.